New Survey: Media and Entertainment Companies are Already Capitalizing on AI

I t’s official—the robots are here. And many media and entertainment (M&E) companies have already started using them to streamline key processes.

Of course, you won’t bump into any of these robots in your company’s break room. Today’s “robots” are software-based artificial intelligence (AI) engines running on servers. And they’re taking over a range of tasks that were previously assigned to humans, from metadata tagging to quality control.

TV Technology magazine, in conjunction with Quantum, surveyed 300 business and technology professionals in the M&E industry to assess AI adoption, identify top use cases, understand typical deployment models, and more. Results show that two-thirds of organizations have not only tested AI technologies, but also adopted them for automating at least one significant workflow process.

Top Use Cases

How are M&E organizations using AI?

Metadata creation and tagging: Nearly half of the survey respondents (47%) use AI for automated metadata creation. Generating and attaching metadata tags to clips can simplify content searches and accelerate retrieval of clips—but few organizations have the time or resources to produce those tags manually. Tagging newly created content is difficult enough, but generating metadata for years of archived content is nearly impossible without automated assistance. So it’s not surprising the survey found that 77% of organizations with large content libraries (more than 20,000 hours of content) use AI for automated metadata creation.

Automated clip generation and distribution: News, sports, and entertainment organizations are increasingly posting snippets of material on social media platforms as a way of attracting new viewers and enhancing existing viewer engagement. With AI technology, organizations can speed up the process of transforming content into compelling social media. More than one-third of survey respondents (36%) use AI technology to identify relevant content, and then generate clips in the right format for each social media platform.

Quality control and measurement: AI automation can also help significantly accelerate the process of checking videos for rendering errors, editing mistakes, omissions, and other problems. In the past, that time-consuming process was relegated to humans. But using AI automation for quality control and measurement can help free up personnel for other tasks. The survey shows that 36% of respondents are already using AI technology in that way.

Automated captioning: One-third of survey respondents (33%) are using AI technology for automated captioning. Adding captions to certain video content is required for meeting accessibility regulations, but it can also help people more easily find media by creating text-based content that accompanies videos. Although AI captioning is not yet perfect, it can still save a lot of time compared with manual transcriptions—and that’s important for organizations that need to apply captions to large volumes of archived material.

Storage and Analysis Preferences

What types of IT environments are organizations using for storing and analyzing content? You don’t have to run AI engines in the same place where you store your video—high-speed networking makes it possible to keep those environments separate. But the survey shows that most organizations do store and process content in the same place.

And surprisingly, relatively few organizations use the public cloud. The majority of respondents use an on-site private cloud or object storage (51%), or an off-site private cloud (26%) for AI processing. Only 23% use an off-site public cloud for AI. The results are similar for storage, with 48% using an on-site private cloud or object storage, 33% choosing an off-site private cloud, and only 18% opting for a public cloud. Security concerns or network connectivity costs might be driving many to keep content in-house or at least in private clouds off-site.

Additional Insights

The TV Technology survey goes even deeper into examining who is using AI and why. For example, it identifies the types of M&E organizations using AI most frequently, and it presents metrics relating to key drivers of AI adoption, including content growth and the challenges related to insufficient use of metadata.